class_docstr: Default YOLO validation transform.
  
  Parameters
  ----------
  width : int
      Image width.
  height : int
      Image height.
  mean : array-like of size 3
      Mean pixel values to be subtracted from image tensor. Default is [0.485, 0.456, 0.406].
  std : array-like of size 3
      Standard deviation to be divided from image. Default is [0.229, 0.224, 0.225].
class_docstr_tok: ["Default","YOLO","validation","transform",".","Parameters","--","--","--","--","--","width",":","int","Image","width",".","height",":","int","Image","height",".","mean",":","array-like","of","size","3","Mean","pixel","values","to","be","subtracted","from","image","tensor",".","Default","is","[","0.485",",","0.456",",","0.406","]",".","std",":","array-like","of","size","3","Standard","deviation","to","be","divided","from","image",".","Default","is","[","0.229",",","0.224",",","0.225","]","."]
docstr: Apply transform to validation image/label.
docstr_tok: ["Apply","transform","to","validation","image\/label","."]
code: def __call__(self, src, label):
      """Apply transform to validation image/label."""
      # resize
      h, w, _ = src.shape
      img = timage.imresize(src, self._width, self._height, interp=9)
      bbox = tbbox.resize(
          label, in_size=(w, h), out_size=(self._width, self._height)
      )
  
      img = mx.nd.image.to_tensor(img)
      img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)
      return img, bbox.astype(img.dtype)
  
file_path: gluon-cv/gluoncv/data/transforms/presets/yolo.py
imports: from __future__ import absolute_import
  import numpy
  import mxnet
  from mxnet import autograd
  from ..None import bbox
  from ..None import image
  from ..None import experimental
label_desc: Use image node_id 2 for calling the YOLO3DefaultValTransform obj's underlying member method code with example usage: obj.__call__(src, label) and returns: img,  bbox